Autism Spectrum Disorder Diagnosis Based on Attentional Feature Fusion Using NasNetMobile and DeiT Networks

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects social interactions, communication, and behavior. Prompt and precise diagnosis is essential for prompt support and intervention. In this study, a deep learning-based framework for diagnosing ASD using facial images has been proposed. The methodology begins with logarithmic transformation for image pre-processing, enhancing contrast and making subtle facial features more distinguishable. Next, feature extraction is performed using NasNetMobile and DeiT networks, where NasNetMobile captures high-level abstract patterns, and the DeiT network focuses on fine-grained facial characteristics relevant to ASD identification. The extracted features are then fused using attentional feature fusion, which adaptively assigns importance to the most discriminative features, ensuring an optimal representation. Finally, classification is conducted using bagging with a support vector machine (SVM) classifier employing a polynomial kernel, enhancing generalization and robustness. Experimental results validate the effectiveness of the proposed approach, achieving 95.77% recall, 95.67% precision, 95.66% F1-score, and 95.67% accuracy, demonstrating its strong potential for assisting in ASD diagnosis through facial image analysis.